The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment
that aims to measure low-energy cosmic-ray antiparticles. GAPS has developed a
new antiparticle identification technique based on exotic atom formation caused
by incident particles, which is achieved by ten layers of Si(Li) detector
tracker in GAPS. The conventional analysis uses the physical quantities of the
reconstructed incident and secondary particles. In parallel with this, we have
developed a complementary approach based on deep neural networks. This paper
presents a new convolutional neural network (CNN) technique. A
three-dimensional CNN takes energy depositions as three-dimensional inputs and
learns to identify their positional/energy correlations. The combination of the
physical quantities and the CNN technique is also investigated. The findings
show that the new technique outperforms existing machine learning-based methods
in particle identification.Comment: 7 pages, 10 figure